One of the key concepts in machine learning we addressed in the previous videos is the distinction between supervised and unsupervised learning. Here’s a summary and reminder.

Supervised learning

In supervised learning there is some known key output or (target value) that we wish to predict based on a given set of input features in our data. The aim of training a supervised learning model is to make a mapping from the input features to the target values.

If the target value is a categorical value, such as a plant species, or a yes / no answer (e.g., does this image contain a flower), then the problem is referred to as a classification problem. The features used to make this prediction might be the dimensions of some part of a plant, or it could be image pixel values, or some other property derived from an image, among many other things.

Supervised machine learning – we know the classes of all the training data

Unsupervised learning

Unsupervised machine learning – we don’t know the class of any of the training data, but look for structure or clusters

Semi-supervised learning

Semi-supervised machine learning – we only know the class of some of the training data

In this instance we might consider semi-supervised learning, in which the techniques of unsupervised learning could be used to cluster the dataset, with the labelled examples found in each cluster then used to identify the class of each cluster.

Alternatively, we could use active learning in which a supervised learning model is trained using the labelled examples, and then used to identify unlabelled examples that if labelled could help the model’s accuracy. Once labelled with some human input, these examples can then be added to repeated training runs.